Neural Networks Kick-Starter with JavaScript

Objective

This session will act as a practical gateway into the world of Artificial Neural Networks. You will leave the session with an in-depth understanding of basic concepts and terminologies of Artificial Neural Network and the practical knowledge of how to create them using JavaScript.

Description

A Neural Network is a broad term used to represent a vast collection of computational models loosely based on the biological synapses and neurons. Its history dates back to 1940s and since then the field has grown leaps and bounds. Today, Artificial Neural Networks are being widely used in Natural Language Processing, Speech Recognition, Stock Market Analysis, Signal Processing etc.

Inspite of being one of the most exciting parts of Computer Science, there is a vast gap between its research and developer community. This session will attempt at bridging that gap by skipping the non-trivial Mathematics that runs it and providing an operative understanding to the audience, that will be allow them to go ahead and experiment right away.

The session will broadly contain:
1. The type of problems a Neural Network can solve
2. Dissecting the Neural Network structure
3. Walk through of the working of a Neural Network
4. Understanding training
5. Dos and Don’ts while using Neural Networks
6. Demo containing construction of Neural Network using Brain.js
7. Resources and Examples

Neural Networks in JavaScript is in its infant stages. While the number libraries & frameworks for Neural Network are generally small, the support for it in JavaScript is just taking off. The “computationally intensive” tag that Neural Networks carry shouldn’t trouble JavaScript anymore and performance can further be enhanced by collaborating with native libraries. Overall, it is an exciting time to get on it without being enslaved to a particular framework and explore the possiblities of tackling problems by deploying Neural Networks client side.

Requirements

Inquisitive nature and a Flair for learning something new!

Speaker bio

Name: Karthik Hebbar C
Work: Computer Scientist @ Adobe Systems

Details:
I have been working with Dreamweaver, an IDE for web development, for past two years with occasional collaboration with Brackets, an open-source editor for Web Development, written in HTML,CSS and JavaScript. As a part of Dreamweaver, I have worked with Chromium Embedded Framework and SpiderMonkey.

As far as Neural Networks is concerned, my run-ins with it has resulted in a couple of projects,
1. An NLP to construct a SPARQL query from the user’s search query. This acted as a core for an experimental semantic search engine.
2. A Recurrent Neural Network to predict Surface Roughness based on the operational parameters of a lathe machine.